Research on regional zenith tropospheric delay based on neural network technology

Zheng, Dunyong; Hu, Wusheng; Wang, Jin; Zhu, Mingchen

doi:10.1179/1752270614y.0000000130

Cited by 22 publications

(10 citation statements)

References 22 publications

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“…Modeling results from the Hopfield and proposed model were compared by reference to the true ZTD. It showed that the proposed model could improve ZTD prediction accuracy by more than 90% [10]. e traditional BPNN model was initialized with rich regional longterm continuously operating reference stations (CORS) information to reduce model parameters.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Modeling of Regional Tropospheric Delay Based on Multicore Support Vector Machine

Yang

Jiang

et al. 2021

Mathematical Problems in Engineering

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Real-time modeling of regional troposphere has attracted considerable research attention in the current GNSS field, and its modeling products play an important role in global navigation satellite system (GNSS) real-time precise positioning and real-time inversion of atmospheric water vapor. Multicore support vector machine (MS) based on genetic optimization algorithm, single-core support vector machine (SVM), four-parameter method (FP), neural network method (BP), and root mean square fusion method (SUM) are used for real-time and final zenith tropospheric delay (ZTD) modeling of Hong Kong CORS network in this study. Real-time ZTD modeling experiment results for five consecutive days showed that the average deviation (bias) and root mean square (RMS) of FP, BP, SVM, and SUM reduced by 48.25%, 54.46%, 41.82%, and 51.82% and 43.16%, 48.46%, 30.09%, and 33.86%, respectively, compared with MS. The final ZTD modeling experiment results showed that the bias and RMS of FP, BP, SVM, and SUM reduced by 3.80%, 49.78%, 25.71%, and 49.35% and 43.16%, 48.46%, 30.09%, and 33.86%, respectively, compared with MS. Accuracy of the five methods generally reaches millimeter level in most of the time periods. MS demonstrates higher precision and stability in the modeling of stations with an elevation at the average level of the survey area and higher elevation than that of other models. MS, SVM, and SUM exhibit higher precision and stability in the modeling of the station with an elevation at the average level of the survey area than FP. Meanwhile, real-time modeling error distribution of the five methods is significantly better than the final modeling. Standard deviation and average real-time modeling improved by 43.19% and 24.04%, respectively.

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Section: Introductionmentioning

confidence: 99%

Real-Time Modeling of Regional Tropospheric Delay Based on Multicore Support Vector Machine

Yang

Jiang

et al. 2021

Mathematical Problems in Engineering

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“…The predicted results showed an MB ranging from À10.9 cm to 11.5 cm and a root mean square error (RMSE) of 3.6 cm. Zheng et al (2015) also employed the same technique to develop a regional ZTD prediction model over Jiangsu Province of China, providing an RMSE of 0.004 m. Likewise, and Ding and Hu (2019) 2017) used the backpropagation neural network (BP NN) technique to develop a regional ZTD model using 15 continuously operating reference stations (CORS) in Hong Kong. The accuracy of their model was reported to be 1.1 cm.…”

Section: Introductionmentioning

confidence: 99%

Regression models for predicting daily IGS zenith tropospheric delays in West Africa: Implication for GNSS meteorology and positioning applications

Osah

Acheampong

Fosu

et al. 2021

Meteorological Applications

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The ability to precisely and accurately model and predict tropospheric delay is essential for precise global navigation satellite system (GNSS) and meteorological applications. The International GNSS Service (IGS) provides highly accurate and highly reliable daily time series zenith tropospheric delay (ZTD) products for all its member sites using data from each IGS site. Nevertheless, if for reasons such as poor internet connectivity, equipment failure, and power outages the IGS station is inaccessible, gaps are created in the data archive, resulting in degrading the quality of the ZTD estimation, as well as inhibits the quality of precipitable water vapour (PWV) estimation, needed for precise positioning applications, meteorological studies, and weather forecasting. To address this challenge, five regression models are proposed in this study to model and predict daily ZTDs using daily datasets from four IGS stations in West Africa over a period of 5 years (2015)(2016)(2017)(2018)(2019). The site-specific Vienna Mapping Functions 3 (VMF3) products (ZTD, pressure, temperature, water vapour partial pressure) and stations' coordinates (latitudes and longitudes) are used as the predictors, while the IGS final ZTD product as the response variable in fitting the models. Several performance measures are calculated to compare the predictive performance of the models.The results show that the five regression models performed outstandingly and agree very well with the IGS-ZTD data, and hence provide a useful alternative for ZTD predictions and also in the event the West African IGS stations' ZTD data are unavailable. Nonetheless, the support vector regression model outperformed the remaining four models.

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“…In this way, a sufficient resolution and accuracy for meteorological forecasts can be achieved. Therefore, tropospheric error interpolation becomes more important, especially for sparse GNSS networks [13]. Spatial interpolation of the tropospheric wet delay is a difficult issue due to the rapid spatial variations of water vapor in the troposphere.…”

Section: Introductionmentioning

confidence: 99%

“…The test results showed that a combination of meteorological parameters, such as relative humidity, air pressure, wet bulb temperature, and cloudiness as an input for the ANN model could significantly improve the forecast accuracy and efficiency. In the literature, numerous ANN studies for meteorological predictions have been carried out based on MLP architecture [13,16,17,21,22]. In addition, different models have been applied for the modeling of atmospheric parameters.…”

Section: Introductionmentioning

confidence: 99%

“…In the analysis, the grid point values were horizontally interpolated to the user position and the mean RMS value was found to be 3.8 cm. In another study conducted by Zheng et al [13], interpolation of the troposphere delay was performed based on the feed-forward neural network model. In this study, artificial neural network tests were carried out using the differences between total troposphere delay obtained from the Hopfield [3] model and total troposphere delay obtained from the GNSS technique.…”

Section: Introductionmentioning

confidence: 99%

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Spatial Interpolation of GNSS Troposphere Wet Delay by a Newly Designed Artificial Neural Network Model

Selbesoglu

2019

Applied Sciences

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Global Navigation Satellite System (GNSS) signals arrive at the Earth in a nonlinear and slightly curved way due to the refraction effect caused by the troposphere. The troposphere delay of the GNSS signal consists of hydrostatic and wet parts. In particular, tropospheric wet delay prediction and interpolation are more difficult than those of the dry component due to the rapid temporal and spatial variation of the water vapor content. Wet delay estimation and interpolation with a sufficient accuracy is an important issue for all parameters obtained by GNSS positioning techniques. In particular, in real-time positioning applications, errors caused by interpolation of the wet troposphere delay are reflected in the height component of about 1 to 2 cm. Furthermore, the amount of water vapor in the troposphere is very important information in weather forecast applications obtained as a function of wet delay. Therefore, real-time monitoring of the troposphere can be achieved with a higher resolution and accuracy by utilizing neural network models for interpolation of the wet tropospheric delay. In addition, in the absence of the GNSS station, wet delays can be interpolated by means of the surrounding stations to the desired location. In this study, a back propagation artificial neural network (BPNN) model based on meteorological parameters obtained from The New Austrian Meteorological Measuring Network (TAWES) was used to interpolate wet troposphere delay. Analysis was carried out at 40 reference stations of the Echtzeit Positionierung Austria (EPOSA) GNSS Network covering the whole of Austria. The interpolation of zenith wet delays based on the artificial neural network was performed by using latitude, longitude, altitude and meteorological parameters (temperature, pressure, weighted mean temperature, and water vapor pressure). These parameters were then subtracted from the artificial neural network model one by one and six different artificial neural networks were designed. In addition, the linear interpolation method (LIN) and inverse distance weighted interpolation method (IDW) were used as conventional interpolation methods. In order to investigate the effect of the network density on interpolation methods, three networks, including 40, 30, and 20 reference stations, were formed and the increased distance effect on interpolation methods was evaluated. In addition, analyses were conducted in winter, spring, and summer to evaluate the seasonal effects on interpolation methods. According to the statistical analysis, the root mean square error (RMSE) values of the IDW, LIN, and BPNN methods were found to be 12.6, 13.4, and 5.9 mm, respectively.

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Research on regional zenith tropospheric delay based on neural network technology

Cited by 22 publications

References 22 publications

Real-Time Modeling of Regional Tropospheric Delay Based on Multicore Support Vector Machine

Real-Time Modeling of Regional Tropospheric Delay Based on Multicore Support Vector Machine

Regression models for predicting daily IGS zenith tropospheric delays in West Africa: Implication for GNSS meteorology and positioning applications

Spatial Interpolation of GNSS Troposphere Wet Delay by a Newly Designed Artificial Neural Network Model

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